Recent work has investigated the mapping from spatial coordinates to scene properties directly through MLPs as \(\mathbf {y}=f(\mathbf {x};\theta )\) . The high-frequency details can be preserved well with the help of positional encoding [1]}, Fourier feature mapping [2]}, or the periodic activation [3]}. We extend the implicit neural representation to a continual learning fashion, where an implicit representation can be directly learned from sequential data without computationally expensive re-training or catastrophic forgetting.
